Interactive tool for visualizing performance data in real-time to enable adaptive performance optimization and feedback

ABSTRACT

An interactive tool is disclosed for visualizing performance data in real-time to enable adaptive performance optimization for an application running on a massively parallel computer system. The interactive tool may be used to visualize network congestion (and other) performance counters for an application as it runs on the parallel system in real-time. Further, a developer may use the interactive tool to experiment with various tuning options and optimization approaches on-the-fly.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.11/863,682, entitled “Interactive Tool for Visualizing Performance Datain Real-Time to Enable Adaptive Performance Optimization and Feedback”,filed Sep. 28, 2007, by Gooding et al. This related patent applicationis herein incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to parallel computing. Morespecifically, the present invention relates to an interactive tool forvisualizing performance data in real-time to enable adaptive performanceoptimization and feedback.

2. Description of the Related Art

Powerful computers may be designed as highly parallel systems where theprocessing activity of hundreds, if not thousands, of processors (CPUs)are coordinated to perform computing tasks. These systems are highlyuseful for a broad variety of applications including, financialmodeling, hydrodynamics, quantum chemistry, astronomy, weather modelingand prediction, geological modeling, prime number factoring, imageprocessing (e.g., CGI animations and rendering), to name but a fewexamples.

For example, one family of parallel computing systems has been (andcontinues to be) developed by International Business Machines (IBM)under the name Blue Gene®. The Blue Gene/L architecture provides ascalable, parallel computer that may be configured with a maximum of65,536 (2¹⁶) compute nodes. Each compute node includes a singleapplication specific integrated circuit (ASIC) with 2 CPU's and memory.The Blue Gene/L architecture has been successful and on Oct. 27, 2005,IBM announced that a Blue Gene/L system had reached an operational speedof 280.6 teraflops (280.6 trillion floating-point operations persecond), making it the fastest computer in the world at that time.Further, as of June 2005, Blue Gene/L installations at various sitesworld-wide were among five out of the ten top most powerful computers inthe world.

In addition to the Blue Gene architecture developed by IBM, other highlyparallel computer systems have been (and are being) developed. Forexample, a Beowulf cluster may be built from a collection of commodityoff-the-shelf personal computers. In a Beowulf cluster, individualsystems are connected using local area network technology (e.g., GigabitEthernet) and system software is used to execute programs written forparallel processing on the cluster of individual systems.

Compute nodes in a parallel system communicate with one another over oneor more communication networks. For example, the compute nodes of a BlueGene/L system are interconnected using five specialized networks, andthe primary communication strategy for the Blue Gene/L system is messagepassing over a torus network (i.e., a set of point-to-point linksbetween pairs of nodes). This message passing allows programs writtenfor parallel processing to use high level interfaces such as MessagePassing Interface (MPI) and Aggregate Remote Memory Copy Interface(ARMCI) to perform computing tasks and to distribute data among a set ofcompute nodes. Other parallel architectures (e.g., a Beowulf cluster)also use MPI and ARMCI for data communication between compute nodes. Lowlevel network interfaces communicate higher level messages using smallmessages known as packets. Typically, MPI messages are encapsulated in aset of packets which are transmitted from a source node to a destinationnode over a communications network (e.g., the torus network of a BlueGene system).

Frequently, network contention is a major problem for the scalability ofan application on a large parallel system. That is, compute nodes maycompete with one another for access to the communication networksinterconnecting the nodes on which the application is executing and asmore compute nodes are dedicated to a given application, the moreinter-node communication is typically required. Thus, it is desirable tooptimize the configuration a given software application, includingoptimizing network communication patterns of the application. Further,communication patterns tend to be different at computational phases ofprogram execution and are often quite complex.

Furthermore, supercomputing resources are a scarce commodity, and accessto a parallel computing system is usually rented and/or allocated insmall discrete blocks of time. When optimizing such an application,therefore, it is important to gather as much information on as manyconfigurations of a parallel system and/or an application as is possiblewithin an allotted time window.

Accordingly, there remains a need for an interactive tool forvisualizing performance data in real-time to enable adaptive performanceoptimization and feedback on a large parallel computing system.

SUMMARY OF THE INVENTION

One embodiment of the invention provides a method a computer-implementedmethod of generating a visual representation of performance data for anapplication running on a plurality of compute nodes of a parallelcomputing system. The method generally includes receiving a selection ofone or more performance counters present on the plurality of computenodes and receiving a value for each of the selected performancecounters. The values may be received without disrupting the performanceof the application running on the plurality of compute nodes. The methodalso includes generating a visual display of the plurality of computenodes that depicts the plurality of compute nodes and depicts aperformance characteristic of the application, as reflected by thereceived values for each of the selected performance counters.

Another embodiment of the invention includes a computer-readable storagemedium containing a program which, when executed, performs an operationof generating a visual representation of performance data for anapplication running on a plurality of compute nodes of a parallelcomputing system. The operation may generally include receiving aselection of one or more performance counters present on the pluralityof compute nodes and receiving a value for each of the selectedperformance counters. The values may be received without disrupting theperformance of the application running on the plurality of computenodes. The operation may further include generating a visual display ofthe plurality of compute nodes that depicts the plurality of computenodes and also depicts a performance characteristic of the application,as reflected by the received values for each of the selected performancecounters.

Yet another embodiment of the invention provides a parallel computingsystem having a plurality of compute nodes executing an application,each of the plurality of compute nodes having at least a processor and amemory. Each of the plurality of compute nodes may include one or moreperformance counters. The parallel system may also include a servicenode having at least a processor and a memory containing an interactiveperformance visualization tool. The visualization tool may be generallyconfigured to receive a selection of one or more of the performancecounters and receive a value for each of the selected performancecounters. The values may be received without disrupting the performanceof the application running on the plurality of compute nodes. Thevisualization too may be further configured to generate a visual displayof the plurality of compute nodes, where the visual display depicts theplurality of compute nodes and also depicts a performance characteristicof the application, as reflected by the received values for each of theselected performance counters.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features, advantages andobjects of the present invention are attained and can be understood indetail, a more particular description of the invention, brieflysummarized above, may be had by reference to the embodiments thereofwhich are illustrated in the appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 is a high-level block diagram of components of a massivelyparallel computer system, according to one embodiment of the presentinvention.

FIG. 2 is a conceptual illustration of a three dimensional torus networkof the system of FIG. 1, according to one embodiment of the invention.

FIG. 3 is a high-level diagram of a compute node of the system of FIG.1, according to one embodiment of the invention.

FIG. 4 is a flow diagram illustrating a method for visualizingperformance data in real-time to enable adaptive performanceoptimization and feedback on a massively parallel computer system,according to one embodiment of the invention.

FIGS. 5A and 5B illustrate an example user interface of an interactivetool for visualizing performance data in real-time to enable adaptiveperformance optimization and feedback, according to one embodiment ofthe invention.

FIG. 6 illustrates a method for generating a visualization ofperformance data in real-time, according to one embodiment of theinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Embodiments of the invention provide an interactive tool for visualizingperformance data in real-time to enable adaptive performanceoptimization for an application running on a massively parallel computersystem. For example, embodiments of the invention may be used todiagnose and alleviate network congestion problems and improveapplication scalability on message passing supercomputers such as theBlue Gene architecture developed by IBM. Of course, embodiments of theinvention may be adapted for use with other parallel systems that usemessage passing for node-to-node communications. In one embodiment, aninteractive tool may be used to visualize the network (and other)performance counters recorded for an application as the application runson the parallel system in real-time. Further, a developer may use theinteractive tool to experiment with various tuning options andoptimization approaches on-the-fly.

The developer may use visual displays generated by the interactive toolto identify bottlenecks in the network communication patterns of anapplication as well as to devise improvements to those bottlenecks.Additionally, by displaying the network performance in real-time, thedeveloper may immediately see the effect of a given change, allowing thedeveloper to evaluate potential changes without having to repeatedlystop and start the application. This approach reduces the amount of timerequired to determine an optimal configuration for a given application.

In the following, reference is made to embodiments of the invention.However, it should be understood that the invention is not limited tospecifically described embodiments. Instead, any combination of thefollowing features and elements, whether related to differentembodiments or not, is contemplated to implement and practice theinvention. Furthermore, in various embodiments the invention providesnumerous advantages over the prior art. However, although embodiments ofthe invention may achieve advantages over other possible solutionsand/or over the prior art, whether or not a particular advantage isachieved by a given embodiment is not limiting of the invention. Thus,the following aspects, features, embodiments and advantages are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s). Likewise,reference to “the invention” shall not be construed as a generalizationof any inventive subject matter disclosed herein and shall not beconsidered to be an element or limitation of the appended claims exceptwhere explicitly recited in a claim(s).

One embodiment of the invention is implemented as a program product foruse with a computer system. The program(s) of the program productdefines functions of the embodiments (including the methods describedherein) and can be contained on a variety of computer-readable media.Illustrative computer-readable media include, but are not limited to:(i) non-writable storage media (e.g., read-only memory devices within acomputer such as CD-ROM or DVD-ROM disks readable by a CD- or DVD-ROMdrive) on which information is permanently stored; (ii) writable storagemedia (e.g., floppy disks within a diskette drive, a hard-disk drive,volatile and non-volatile memory such as flash and dynamic random accessmemory) on which alterable information is stored. Other media includecommunications media through which information is conveyed to acomputer, such as through a computer or telephone network, includingwireless communications networks. The latter embodiment specificallyincludes transmitting information to/from the Internet and othernetworks. Such computer-readable media, when carrying computer-readableinstructions that direct the functions of the present invention,represent embodiments of the present invention.

In general, the routines executed to implement the embodiments of theinvention, may be part of an operating system or a specific application,component, program, module, object, or sequence of instructions. Thecomputer program of the present invention typically is comprised of amultitude of instructions that will be translated by the native computerinto a machine-readable format and hence executable instructions. Also,programs are comprised of variables and data structures that eitherreside locally to the program or are found in memory or on storagedevices. In addition, various programs described hereinafter may beidentified based upon the application for which they are implemented ina specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature.

FIG. 1 is a high-level block diagram of components of a massivelyparallel computer system 100, according to one embodiment of the presentinvention. Illustratively, computer system 100 shows the high-levelarchitecture of an IBM Blue Gene® computer system, it being understoodthat other parallel computer systems could be used, and the descriptionof a preferred embodiment herein is not intended to limit the presentinvention.

As shown, computer system 100 includes a compute core 101 having anumber of compute nodes arranged in a regular array or matrix, whichperform the useful work performed by system 100. The operation ofcomputer system 100, including compute core 101, may be controlled byservice node 102. Various additional processors in front-end nodes 103may perform auxiliary data processing functions, and file servers 104provide an interface to data storage devices such as disk based storage109A, 109B or other I/O (not shown). Functional network 105 provides theprimary data communication path among compute core 101 and other systemcomponents. For example, data stored in storage devices attached to fileservers 104 is loaded and stored to other system components throughfunctional network 105.

Also as shown, compute core 101 includes I/O nodes 111A-C and computenodes 112A-I. Compute nodes 112 provide the processing capacity ofparallel system 100 and are configured to execute applications writtenfor parallel processing. I/O nodes 111 handle I/O operations on behalfof compute nodes 112. Each I/O node 111 may include a processor andinterface hardware that handles I/O operations for a set of N computenodes 112, the I/O node and its respective set of N compute nodes arereferred to as a Pset. Compute core 101 contains M Psets 115A-C, eachincluding a single I/O node 111 and N compute nodes 112, for a total ofM×N compute nodes 112. The product M×N can be very large. For example,in one implementation M=1024 (1K) and N=64, for a total of 64K computenodes.

In general, application programming code and other data input requiredby compute core 101 to execute user applications, as well as data outputproduced by the compute core 101, is communicated over functionalnetwork 105. The compute nodes within a Pset 115 communicate with thecorresponding I/O node over a corresponding local I/O tree network113A-C. The I/O nodes, in turn, are connected to functional network 105,over which they communicate with I/O devices attached to file servers104, or with other system components. Thus, the local I/O tree networks113 may be viewed logically as extensions of functional network 105, andlike functional network 105 are used for data I/O, although they arephysically separated from functional network 105.

In a Blue Gene system, compute nodes 112 are connected by multipleindependent networks. Other massively parallel architectures also usemultiple networks for node-to-node communication. Specifically, in aBlue Gene system, a torus network connects the compute nodes 112 in a 3Dmesh with wrap around links. Each compute node 112 is connected to itssix neighbors through the torus network, and is addressed by an (x, y,z) coordinate in within the mesh. Thus, each compute node 112 maytransmit a message directly to a neighboring node in the X+ and X−, theY+ and Y−, and the Z+ and Z− directions.

FIG. 2 is a conceptual illustration of a three-dimensional torus networkof system 100, according to one embodiment of the invention. Morespecifically, FIG. 2 illustrates a 4×4×4 torus 201 of compute nodes, inwhich the interior nodes are omitted for clarity. Although FIG. 2 showsa 4×4×4 torus having 64 nodes, it will be understood that the actualnumber of compute nodes in a parallel computing system is typically muchlarger. For example, a complete Blue Gene/L system includes 65,536compute nodes. Each compute node 112 in torus 201 includes a set of sixnode-to-node communication links 202A-F which allows each compute nodesin torus 201 to communicate with its six immediate neighbors, two nodesin each of the x, y and z coordinate dimensions.

As used herein, the term “torus” includes any regular pattern of nodesand inter-nodal data communications paths in more than one dimension,such that each node has a defined set of neighbors, and for any givennode, it is possible to determine the set of neighbors of that node. A“neighbor” of a given node is any node which is linked to the given nodeby a direct inter-nodal data communications path. That is, a path whichdoes not have to traverse another node. The compute nodes may be linkedin a three-dimensional torus 201, as shown in FIG. 2, but may also beconfigured to have more or fewer dimensions. Also, it is not necessarilythe case that a given node's neighbors are the physically closest nodesto the given node, although it is generally desirable to arrange thenodes in such a manner, insofar as possible.

The compute nodes in each of the x, y, or z dimensions form a torus inthat dimension because the point-to-point communication links logicallywrap around. As shown, for example, links 202D, 202E, and 202F whichwrap around from compute node 203 to other end of compute core 201 ineach of the x, y and z dimensions. Thus, although node 203 appears to beat a “corner” of the torus, node-to-node links 202A-F link node 203 tonodes 204, 205, and 206, in the x, y, and Z dimensions of torus 201.

Referring again to FIG. 1, another network on the Blue Gene system is aglobal combining network (i.e., tree network 113), which connectscompute nodes 112 in a binary tree. In the combining network, eachcompute node 112 has a parent and two children (although some nodes mayhave zero or one child, depending on the hardware configuration). It isalso important to note that in the Blue Gene architecture, the tree andtorus networks are independent networks. That is, these networks do notshare network resources such as links or packet injection FIFOs.Communication networks in other parallel architectures have similarcharacteristics.

A third network on a Blue Gene system is the JTAG (Joint Test ActionGroup) network (i.e., control system network 106), which may beconfigured to provide a hardware monitoring facility. As is known, JTAGis a standard for providing external test access to integrated circuitsserially, via a four- or five-pin external interface. The JTAG standardhas been adopted as an IEEE standard. Within the Blue Gene system, theJTAG network may be used to send performance counter data to servicenode 102 in real-time. That is, while an application is running oncompute core 101, performance data may be gathered and transmitted toservice node 102 without affecting the performance of that application.For example, in one embodiment, the JTAG network may be used to recordfloating point and cache performance on a given compute node 112, alongwith the number of network packets that pass through any of the sixnetwork ports (X+, X−, Y+, Y−, Z+, Z−) on that compute node 112. Thesehardware counters allow the network traffic (and memory accessperformance) to be monitored without affecting the performance of theapplication being monitored. Further, in one embodiment, the JTAGnetwork may be used to modify the operational state of compute core 101while an application is running, without disrupting applicationperformance. For example, the JTAG network may be used to specify whatmessage passing protocol should be used by compute nodes 112 (or toadjust a configuration for a current message passing protocols).

Service node 102 communicates control and state information with thenodes of compute core 101 over control system network 106. Network 106is coupled to a set of hardware controllers 108A-C (e.g., a JTAGcontroller). Each hardware controller communicates with the nodes of arespective Pset 115 over a corresponding local hardware control network114A-C. The hardware controllers 108 and local hardware control networks114 are logically an extension of control system network 106, althoughphysically separate.

In one embodiment, service node 102 may be configured to direct and/ormonitor the operation of the compute nodes 112 in compute core 101.Service node 102 is a computer that includes a processor (or processors)121, internal memory 120, and local storage 125. A display device 107provides an LCD or CRT display monitor. Illustratively, memory 120 ofservice node 102 includes a control system 122, an MPI runtimecontroller 123, a performance counter query tool 124, and avisualization tool 126.

Control system 122 may be a software application configured to controlthe allocation of compute nodes 112 in compute core 101, direct theloading of application and data on compute nodes 111, and performdiagnostic and maintenance functions, among other things. MPI runtimecontroller 124 may be a software application used to configure theprotocols used by compute nodes 111 to communicate using MPI (or other)messages. For example, MPI runtime controller 124 may be used to selectbetween message passing strategies such as the well-known “eager” and“rendezvous” protocols. When using the eager protocol, a sending nodeassumes that a receiving node can receive and store the message if it issent. The receiving node has the responsibility to buffer the messageupon its arrival. The eager protocol is generally used for smallermessage sizes (typically up to Kbytes in size). The rendezvous protocolis used when assumptions about the receiving process buffer space cannotbe made, or when a message size limit specified for the eager protocolis exceeded. The rendezvous protocol requires some type of “handshaking”between the sender and the receiver processes. Typically, in arendezvous implementation, the sender must first send a request andreceive an acknowledgment before a message may be transferred.

In one embodiment, performance counter query tool 124 may be a softwareapplication configured to query and report on performance counter datareceived over control system network 106. For example, query tool 124may collect the counter values representing the number of networkpackets that pass through any of the six network ports (X+, X−, Y+, Y−,Z+, Z−) on a given compute node 112. Visualization tool 126 may be asoftware application configured to use the performance counter datareceived from query tool 124 and generate a visual representation of theperformance of parallel computing system 100. For example, thevisualization tool 126 may be configured to alter the colors used todepict a compute node as the counter value increases. In such a case,the color may depend on the traffic that passes through a single networkport (e.g., X+) or may be a composite of all traffic through a givennode. To provide a visual display reflecting network contention, computenodes 112 in a state of high contention may be displayed more red thancompute nodes 112 in a state of low contention. Further, in oneembodiment, visualization tool 126 may also provide a variety ofvisualization techniques which could be employed by the developer (e.g.,a display of a selected slice of the compute nodes or a fly-throughanimation, etc.).

In addition to service node 102, front-end nodes 103 provide computersystems used to perform auxiliary functions which, for efficiency orotherwise, are best performed outside compute core 101. Functions whichinvolve substantial I/O operations are generally performed in thefront-end nodes. For example, interactive data input, application codeediting, or other user interface functions are generally handled byfront-end nodes 103, as is application code compilation. Front-end nodes103 are connected to functional network 105 and may communicate withfile servers 104.

FIG. 3 is a high-level diagram of a compute node 112 of the system 100of FIG. 1, according to one embodiment of the invention. As shown,compute node 112 includes processor cores 301A and 301B, and alsoincludes memory 302 used by both processor cores 301; an externalcontrol interface 303 which is coupled to local hardware control network114; an external data communications interface 304 which is coupled tothe corresponding local I/O tree network 113, and the corresponding sixnode-to-node links 202 of the torus network 201; and monitoring andcontrol logic 305 which receives and responds to control commandsreceived through external control interface 303. Monitoring and controllogic 305 may access processor cores 301 and locations in memory 302 onbehalf of service node 102 to read (or in some cases alter) theoperational state of node 112. In one embodiment, each node 112 may bephysically implemented as a single, discrete integrated circuit chip.

As described, functional network 105 may service many I/O nodes, andeach I/O node is shared by multiple compute nodes 112. Thus, it isapparent that the I/O resources of parallel system 100 are relativelysparse when compared to computing resources. Although it is a generalpurpose computing machine, parallel system 100 is designed for maximumefficiency in applications which are computationally intense.

As shown in FIG. 3, memory 302 stores an operating system image 311, anapplication code image 312, and user application data structures 313 asrequired. Some portion of memory 302 may be allocated as a file cache314, i.e., a cache of data read from or to be written to an I/O file.Operating system image 311 provides a copy of a simplified-functionoperating system running on compute node 112. Operating system image 311may includes a minimal set of functions required to support operation ofthe compute node 112. In a Blue Gene system, for example, operatingsystem image 311 contains a version of the Linux® operating systemcustomized to run on compute node 112. Of course, other operatingsystems may be used, and further it is not necessary that all nodesemploy the same operating system. (Also note, Linux® is a registeredtrademark of Linus Torvalds in the United States and other countries.)

Application code image 312 represents a copy of the application codebeing executed by compute node 112. Application code image 302 mayinclude a copy of a computer program being executed by system 100. Inone embodiment, each node may execute an identical copy of the sameapplication, where each copy is configured to cooperate with others.Alternatively, an application may be configured as a collection ofdissimilar components configured to perform specialized tasks as part ofthe parallel application processing. Memory 302 may also include acall-return stack 315 for storing the states of procedures which must bereturned to, which is shown separate from application code image 302,although it may be considered part of application code state data.

As part of ongoing operations, application 312 may be configured totransmit packets from compute node 112 to other compute nodes inparallel system 100. For example, the high level MPI call of MPI_Send(); may be used by application 312 to transmit a message (encapsulated ina sequence of packets) from one compute node to another. On the otherside of the communication, the receiving node may invoke the MPI callMPI_Receive( ); to receive and process the message. As described above,in one embodiment, each compute node 112 may include hardware basedcounters configured to count now many packets are passed over theexternal data interface 304 of a given compute node 112 and report thisinformation to service node 102 over control system network 106 usingcontrol network interface 303.

FIG. 4 is a flow diagram illustrating a method for visualizingperformance data in real-time to enable adaptive performanceoptimization and feedback on a massively parallel computer system,according to one embodiment of the invention. As shown, the method 400begins at step 405, where data for a selected performance counter isreceived. For example, as stated, the performance counters may recordthe number of messages that pass through a given network port (e.g. X+)of each compute node 112 running a given application.

At step 410, a visual display is generated that shows the performance ofthe parallel system. The visual display depicts the performance counterdata received at step 405. For example, the visual representation maydepict network congestion on the 3-D torus of a Blue Gene system. Doingso may allow application developers and other users to detect “hotspots”(i.e., areas of significant network congestion), patterns of usage andpotential inefficiency, etc. In one embodiment, the visual display maybe generated and displayed in real-time. That is, it may be generatedand displayed while the application is actively executing on the computenodes of the parallel system. Further, the visual display may be updatedin real-time to reflect the ongoing performance of the applicationrunning on the parallel computing system as different functions areperformed.

By reviewing the visual display, the developer may formulate possiblechanges to the configuration of the parallel system to increase theperformance and scalability of the application. At step 415, thevisualization tool 126 determines whether the system configuration hasbeen modified. If not, then the method returns to step 405 and continuesto update the visual representation of application performance on theparallel system.

Otherwise, at step 420, the parallel system configuration and/or themonitored performance counters may be updated. For example, thedeveloper may experiment with application performance in real-time bytrying different routing protocols or by modifying the values of networkcommunication parameters. In such a case, the developer could switch therouting protocol from eager to rendezvous (or vice versa), or couldexplore alternative routing schemes (e.g., transporter nodes, implicitbarrier remapping, alternative static routing heuristics, etc.), adjustMPI environment variables, and other experiments to evaluate how networkperformance (and thus application scaling) is affected in real-time. Inone embodiment, service node 102 may communicate changes to theconfiguration of the parallel system using control system 106 (e.g., viathe JTAG network of a Blue Gene system). Depending on the particularchange, the change could take affect immediately (e.g., a hardwareconfiguration change). In other cases, the change could take affect atthe next stage of the application (e.g., if MPI messages are needed tochange protocols at a defined state or change which performance countersare monitored).

At step 425, if the developer wishes to continue monitoring, then themethod 400 returns to step 405. Otherwise, at step 430, data recordedfrom a given session may be stored. For example, the performance counterdata may be stored for later differential analysis and/or a time-lapsepayback. That is, visualization tool may be configured to display thedifferences between two performance measurements. In such a case, thevisualization tool may be used generate a display by subtracting theperformance counter data before and after a change was made to thenetwork routing. This would allow the performance changes caused by anexperiment to be highlighted. For example, the difference betweenexecuting an application on the compute nodes of a parallel system usingthe eager protocol versus the rendezvous protocol could be displayed.

Further, in one embodiment, the visualization tool may facilitate ananalysis of the communication patterns of an application over time.Storing the performance counter data allows it to be used to generate atime-lapse animation sequence of application performance. The developermay use an interface provided by visualization tool 126 to rewind backto previously viewed performance data, pause to study a certain instanceof network contention, fast forward at a user selected speed (½ times(X) faster, 1×, 2×, 3×, etc.), skip forward or backward to certainphases, etc.

FIGS. 5A and 5B illustrate an example user interface of an interactivetool for visualizing performance data in real-time to enable adaptiveperformance optimization and feedback, according to one embodiment ofthe invention. As shown in FIG. 5A, visual display 500 depicts arepresentation of an application's network communication patternprojected onto the compute nodes of a three-dimensional torus network510 (e.g., a torus network of a Blue Gene system). Illustratively, thetorus network 510 illustrated in visual display 500 has a high degree ofnetwork congestion on the node-to-node communication links, representedby the dense level of darkly shaded regions of torus network 510. Also,a dialog box 515 includes a node selection tool 505 used to specify andquery for the performance data related to a given compute node 112.Dialog box 515 also includes an MPI configuration tool 520. In thisexample, MPI configuration tool 520 has been used to set MPIcommunications on the torus network to use the rendezvous protocol.

FIG. 5B shows a visual display 550 after reconfiguring the applicationrunning on torus network 510 to use the eager protocol for MPI messagepassing. MPI configuration tool 520 of dialog box 575 now shows that theeager protocol has been selected. In this example, assume that usingeager protocol leads to substantially less network congestion for theapplication running on the torus network 510. Accordingly, the relativelevel of congestion shown on the torus network 510 shown in visualdisplay 550 depicts minimally congested links, relative to the amount ofnetwork congestion in visual display 500, illustrating the reduction innetwork contention that resulted from the change from the rendezvousprotocol to the eager protocol.

FIG. 6 illustrates a method 600 for generating a visualization ofperformance data in real-time, such as the visualization of torus 510shown in FIGS. 5A and 5B, according to one embodiment of the invention.The method 600 begins at step 605 where visualization tool 126 maydetermine an active partition currently running an application. Forexample, on a Blue Gene system, applications run on a block ofpartitions composed from compute nodes in an x, y, and z dimension. Whenthe dimensions are the same (i.e., when x=y=z), the partition forms theshape of a cube, such as the torus 510 shown in FIG. 5A.

At step 610, the visualization tool 126 may generate a 3D wireframerepresenting the partition dimensions. And at step 615, thevisualization tool 126 may load performance data related to theapplication being run on the parallel system. As described above, in oneembodiment, the performance data may be collected through a JTAG networkindependently from the actions of the application. That is, theperformance data may be collected without having an impact on theperformance of the running application. At step 620, a user may selectwhich performance counters the user would like to visualize. Forexample, as described, performance counters may record the number ofnetwork packets injected on each of the six point-to-point links fromeach compute node in the partition running the application. Otherperformance counters include the number of floating point multiplyoperations performed at each compute node, or memory utilizationcounters, e.g., the number of cache misses/hits or the number of DDRpaging operations, performed at each compute node.

At step 625, the visualization tool 126 may determine a minimum andmaximum value for the performance counter selected at step 620. In oneembodiment, the range between the maximum and minimum value is used todetermine a weighted color value for the performance counter for eachnode. For example, if the performance data for network traffic rangedfrom 0 to 200 packets per node, this information could be visualized byassigning different color values to different ranges of packets. Thus,the color values assigned to the busiest node (e.g., nodes in the top10% with 180-200 packets) could be painted on the wireframe in red. Incontrast, color values assigned to the least busy node (e.g., nodes inthe bottom 10% with 0-20 packets) could be painted on the wireframe inblue. Further, in one embodiment, thresholds may be set to preventintermittent traffic from cluttering the visual display. For example, athreshold may require that a node exhibit a minimum amount of networktraffic before being shown on the visualization.

Once the user has specified what performance data to be visualized, andthe visualization tool 126 has retrieved this data, at step 630 a loopbegins to generate a visual display showing the performance of theapplication running on the partition, relative to the performancecounter selected at step 620. At step 635, the visualization tool 126determines whether the counter data for a current node is within anyminimum or maximum thresholds specified by the user. If not, then toolreturns to step 630 and evaluates the performance counter data foranother node. Otherwise, at step 640, visualization tool 126 calculatesa weighed color value for the current node, based on the range of valuesfor the selected performance counter and the value of the performancecounter for the current node. At step 645, the color value determined atstep 640 is used to color a portion of the 3D wireframe generated atstep 610. In particular, a position corresponding to the coordinateposition of the current node within the partition.

In one embodiment, the process of loading performance data (step 615)and the loop of steps 630, 635, 640, and 645, may continue until theuser decides either to quit or to modify the system configuration atstep 650. Thus, the visualization may take on the appearance of ananimation showing “hot” regions of the application running on thepartition as the performance characteristics of the application changeover time.

Advantageously, embodiments of the invention may be used to increase theperformance and scalability of an application running on a massivelyparallel computing system, such as a Blue Gene system. The performancevisualization tool disclosed herein may be used to visualize the network(and other) performance counters of the parallel system duringapplication execution in real-time. Further, a developer may experimentwith various tuning options and optimization approaches on-the-fly. Theability to visualize the network performance of an application as itexecutes allows the developer to identify bottlenecks in the networkcommunication patterns of the application and thus devise improvementsto those bottlenecks. Moreover, by displaying the network performance inreal-time, the developer may immediately see the effect of a givenchange, allowing the developer to evaluate potential changes withouthaving to repeatedly stop and start the application.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A non-transitory computer-readable mediumcontaining a program which, when executed, performs an operation ofgenerating a visual representation of performance data for anapplication running on a plurality of compute nodes of a parallelcomputing system, wherein instances of the application communicate withone another using a first network, the operation comprising: receiving avalue for each of one or more dedicated hardware performance counterspresent on the plurality of compute nodes, wherein the values arereceived without disrupting the performance of the application runningon the plurality of compute nodes by using a second network that isseparate from the first network, and wherein at least one of thededicated hardware performance counters is configured to count a numberof packets that pass through a plurality of network ports coupled to oneof the plurality of compute nodes; determining a maximum value for eachof the dedicated hardware performance counters and a minimum value foreach of the dedicated hardware performance counters, wherein the maximumvalue represents the maximum value for the dedicated hardwareperformance counter across all of the plurality of compute nodes, andwherein the minimum value represents the minimum value for the dedicatedhardware performance counter across all of the plurality of computenodes; for each of the plurality of compute nodes, determining aweighted color value relative to the received values for the othercompute nodes in the plurality of compute nodes, wherein the weightedcolor value is based on the received value for each respective computenode, the respective maximum value for the performance counter, and therespective minimum value for the performance counter; and generating athree-dimensional visual display representing the plurality of computenodes, wherein the three-dimensional visual display depicts a networktopology of the plurality of compute nodes, and wherein thethree-dimensional visual display is colored based on the weighted colorvalue determined for each of the plurality of compute nodes.
 2. Thenon-transitory computer-readable medium of claim 1, wherein thecommunication links interconnect the plurality of compute nodes as amulti-dimensional torus.
 3. The non-transitory computer-readable mediumclaim 1, wherein the plurality of compute nodes pass messages using theMessage Passing Interface (MPI) protocol.
 4. The non-transitorycomputer-readable medium of claim 1, wherein a second one of thededicated hardware performance counters measures one of a floating pointperformance and a cache performance on a given compute node of theplurality of compute nodes.
 5. The non-transitory computer-readablemedium of claim 1, wherein the operation further comprises: modifying aconfiguration setting of the plurality of compute nodes of the parallelcomputing system, wherein the configuration setting may be modifiedwithout disrupting the application running on the plurality of computenodes; and receiving an updated value for each of the dedicated hardwareperformance counters, wherein the updated values are received withoutdisrupting the performance of the application running on the pluralityof compute nodes by using a dedicated, second network that is separatefrom the first network; and generating an updated three-dimensionalvisual display representing the plurality of compute nodes, wherein theupdated three-dimensional visual display depicts the network topology ofthe plurality of compute nodes, and wherein the updatedthree-dimensional visual display is colored based on an updated weightedcolor value determined for each of the plurality of compute nodes, basedon the received updates value for each of the plurality of computenodes.
 6. The non-transitory computer-readable medium of claim 5,wherein the operation further comprises storing the generatedthree-dimensional visual display and the updated three-dimensionalvisual display.
 7. The non-transitory computer-readable medium of claim1, wherein the operation further comprises, generating a time-lapseanimation depicting changes in the dedicated hardware performancecounters that occur while the application is running on the parallelsystem.
 8. A parallel computing system, comprising: a plurality ofcompute nodes executing an application, each of the plurality of computenodes having at least a processor and a memory, wherein each of theplurality of compute nodes includes one or more dedicated hardwareperformance counters, and wherein instances of the applicationcommunicate with one another using a first network; and a service nodehaving at least a processor and a memory containing an interactiveperformance visualization tool, wherein the visualization tool isconfigured to: receive a value for each of the one or more dedicatedhardware performance counters, wherein the values are received withoutdisrupting the performance of the application running on the pluralityof compute nodes, by using a second network that is separate from thefirst network, and wherein at least one of the dedicated hardwareperformance counters is configured to count a number of packets thatpass through a plurality of network ports coupled to one of theplurality of compute nodes, determine a maximum value for each of thededicated hardware performance counters and a minimum value for each ofthe dedicated hardware performance counters, wherein the maximum valuerepresents the maximum value for the dedicated hardware performancecounter across all of the plurality of compute nodes, and wherein theminimum value represents the minimum value for the dedicated hardwareperformance counter across all of the plurality of compute nodes; foreach of the plurality of compute nodes, determine a weighted color valuerelative to the received values for the other compute nodes in theplurality of compute nodes, wherein the weighted color value is based onthe received value for each respective compute node, the respectivemaximum value for the performance counter, and the respective minimumvalue for the performance counter; and generate a three-dimensionalvisual display representing the plurality of compute nodes, wherein thethree-dimensional visual display depicts a network topology of theplurality of compute nodes, and wherein the three-dimensional visualdisplay is colored based on the weighted color value determined for eachof the plurality of compute nodes.
 9. The parallel computing system ofclaim 8, wherein the communication links interconnect the plurality ofcompute nodes as a multi-dimensional torus.
 10. The parallel computingsystem claim 8, wherein the plurality of compute nodes pass messagesusing the Message Passing Interface (MPI) protocol.
 11. The parallelcomputing system of claim 8, wherein a second one of the one or moreperformance counters measures one of a floating point performance and acache performance on a given compute node of the plurality of computenodes.
 12. The parallel computing system of claim 8, wherein thevisualization tool is further configured to: modify a configurationsetting of the plurality of compute nodes of the parallel computingsystem, wherein the configuration setting may be modified withoutdisrupting the application running on the plurality of compute nodes;receive an updated value for each of the dedicated hardware performancecounters, wherein the updated values are received without disrupting theperformance of the application running on the plurality of compute nodesby using a dedicated, second network that is separate from the firstnetwork; and generate an updated three-dimensional visual displayrepresenting the plurality of compute nodes, wherein the updatedthree-dimensional visual display depicts the network topology of theplurality of compute nodes, and wherein the updated three-dimensionalvisual display is colored based on an updated weighted color valuedetermined for each of the plurality of compute nodes, based on thereceived updates value for each of the plurality of compute nodes. 13.The parallel computing system of claim 12, wherein the visualizationtool is further configured to store the generated three-dimensionalvisual display and the updated three-dimensional visual display.
 14. Theparallel computing system of claim 13, wherein the visualization tool isfurther configured to generate a time-lapse animation depicting changesin the dedicated hardware performance counters that occur while theapplication is running on the parallel system.
 15. A non-transitorycomputer-readable medium containing a program which, when executed,performs an operation of generating a visual representation ofperformance data for an application running on a plurality of computenodes of a parallel computing system, wherein instances of theapplication communicate with one another using a first network, theoperation comprising: receiving a value for each of one or morededicated hardware performance counters present on the plurality ofcompute nodes, wherein the values are received without disrupting theperformance of the application running on the plurality of compute nodesby using a second network that is separate from the first network, andwherein at least one of the dedicated hardware performance counters isconfigured to count a number of packets that pass through a plurality ofnetwork ports coupled to one of the plurality of compute nodes;modifying a configuration setting of the plurality of compute nodes ofthe parallel computing system, wherein the configuration setting may bemodified without disrupting the application running on the plurality ofcompute nodes; receiving an updated value for each of the dedicatedhardware performance counters, wherein the updated values are receivedwithout disrupting the performance of the application running on theplurality of compute nodes, by using the second network; calculating adifference between the received value and the received updated valuesfor each of the plurality of compute nodes for each of the one or morededicated hardware performance counters; determining a maximumdifference value for the dedicated hardware performance counter and aminimum difference value for the dedicated hardware performance counter,wherein the maximum difference value represents the maximum value of thecalculated difference for the dedicated hardware performance counteracross all of the plurality of compute nodes, and wherein the minimumdifference value represents the minimum value of the calculateddifference for the dedicated hardware performance counter across all ofthe plurality of compute nodes; for each of the plurality of computenodes, determining a weighted color value for the calculated differencefor each of the plurality of compute nodes relative to the calculateddifferences for the other compute nodes in the plurality of computenodes, wherein the weighted color value is based on the value of thecalculated difference for the respective compute node, the maximumdifference value, and the minimum difference value; and generating athree-dimensional (3D) wireframe image representing the plurality ofcompute nodes, wherein the 3D wireframe image depicts the plurality ofcompute nodes and further depicts the calculated difference for each ofthe plurality of compute nodes, and wherein the 3D wireframe image iscolored based on the weighted color value determined for each of theplurality of compute nodes.
 16. The non-transitory computer-readablemedium of claim 15, wherein the dedicated hardware performance countersmeasure a number of network packets injected onto a point-to-pointnetwork connecting each of the plurality of compute nodes to one or moreneighboring compute nodes.
 17. The non-transitory computer-readablemedium of claim 16, wherein the point-to-point network interconnects theplurality of compute nodes to form a multi-dimensional torus.
 18. Thenon-transitory computer-readable medium of claim 15, wherein theperformance counter records a number of floating point operationsperformed by each of the plurality of compute nodes.
 19. Thenon-transitory computer-readable medium of claim 15, wherein theperformance counter records a number of cache hits/misses that occurs oneach of the plurality of compute nodes.